Search Results (9,149)

How do regulatory policies, funding structures, and cross-sector coordination shape knowledge flows and institutional transformation? Focusing on the smart medical device sector in Taiwan, this study explores how governance dynamics accelerate system transformation and foster demand for adaptive and integrative innovation systems. Building on the National Biotechnology Innovation System framework and qualitative system dynamics modeling, the study analyzes institutional interactions through 28 semi-structured interviews and 18 policy documents. Findings identify systemic bottlenecks, including translational gaps, coordination challenges, and barriers for traditional manufacturers. These gaps have enabled tech firms to emerge as system leaders by bridging these institutional gaps. This study extends innovation systems theory by conceptualizing an emergent governance function that addresses institutional gaps. At the policy level, the study highlights the importance of enabling institutional change in governance to address structural fragmentation and support system-wide transformation. Full article

Catalysis research is complex and interdisciplinary, involving diverse physical effects and challenging data practices. Research data often captures only selected aspects, such as specific reactants and products, limiting its utility for machine learning and the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) workflows. To improve this, semantic structuring through ontologies is essential. This work extends the established ontologies by refining logical relations and integrating semantic tools such as the Web Ontology Language or the Shape Constraint Language. It incorporates application programming interfaces from chemical databases, such as the Kyoto Encyclopedia of Genes and Genomes and the National Institute of Health’s PubChem database, and builds upon established ontologies. A key innovation lies in automatically decomposing chemical substances through database entries and chemical identifier representations to identify functional groups, enabling more generalized reaction classification. Using new semantic functionality, functional groups are flexibly addressed, improving the classification of reactions such as saponification and ester cleavage with simultaneous oxidation. A graphical interface (GUI) supports user interaction with the knowledge graph, enabling ontological reasoning and querying. This approach demonstrates improved specificity of the newly established ontology over its predecessors and offers a more user-friendly interface for engaging with structured chemical knowledge. Future work will focus on expanding ontology coverage to support a wider range of reactions in catalysis research. Full article

Achieving synergies between government-led and market-based policy instruments is critical to advancing Energy–Environmental Productivity and Sustainable Urban Development. This study investigates the effects of China’s dual-pilot energy policies (New Energy Demonstration Cities (NEDCs) and Energy Consumption Permit Trading (ECPT)) on urban environmental productivity (UEP) across 279 prefecture-level cities from 2006 to 2023. Utilizing a Non-Radial Directional Distance Function (NDDF) approach, combined with Difference-in-Differences (DID) estimation and spatial econometric models, the analysis reveals that these synergistic policies significantly enhance both comprehensive and net measures of UEP. Mechanism analysis highlights the roles of industrial restructuring, technological innovation, and energy transition in driving these improvements, while heterogeneity analysis indicates varying effects across different city types. Spatial spillover analysis further demonstrates that policy impacts extend beyond targeted cities, contributing to broader regional gains in UEP. These findings offer important insights for the design of integrated energy and environmental policies and support progress toward key Sustainable Development Goals (SDG 7, SDG 11, and SDG 12). Full article

Morocco is facing a situation of alarming water stress, aggravated by climate change, overexploitation of resources, and unequal distribution of water, placing the country among the most vulnerable to water scarcity in the MENA region. This study aims to investigate the dynamic relationship between economic growth and water stress in Morocco while highlighting the importance of integrated water management and adaptive economic policies to enhance resilience to water scarcity. A mixed methodology, integrating both qualitative and quantitative methods, was adopted to overview the economic–environmental Moroccan context, and to empirically analyze the GDP (gross domestic product) and water stress in Morocco over the period 1975–2021 using an Autoregressive Distributed Lag (ARDL) approach. The empirical analysis is based on annual data sourced from the World Bank and FAO databases for GDP, agricultural value added, renewable internal freshwater resources, and water productivity. The results suggest that water productivity has a significant positive effect on economic growth, while the impacts of agricultural value added and renewable water resources are less significant and vary depending on the model specification. Diagnostic tests confirm the reliability of the ARDL model; however, the presence of outliers in certain years reflects the influence of exogenous shocks, such as severe droughts or policy changes, on the Moroccan economy. The key contribution of this study lies in the fact that it is the first to analyze the intrinsic link between economic growth and the environmental aspect of water in Morocco. According to our findings, it is imperative to continuously improve water productivity and adopt adaptive management, rooted in science and innovation, in order to ensure water security and support the sustainable economic development of Morocco. Full article

The integration of artificial intelligence (AI), big data analytics, and blockchain technologies within the digital economy presents transformative opportunities for promoting low-carbon urban development. However, a systematic understanding of how these digital innovations influence urban carbon mitigation remains limited. This study addresses this gap by proposing two research questions (RQs): (1) What are the key success factors for artificial intelligence, big data, and blockchain in urban carbon emission reduction? (2) How do these technologies interact and support the transition to low-carbon cities? To answer these questions, the study employs a hybrid methodological framework combining the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques. The data were collected through structured expert questionnaires, enabling the identification and hierarchical analysis of twelve critical success factors (CSFs). Grounded in sustainability transitions theory and institutional theory, the CSFs are categorized into three dimensions: (1) digital infrastructure and technological applications; (2) digital transformation of industry and economy; (3) sustainable urban governance. The results reveal that e-commerce and sustainable logistics, the adoption of the circular economy, and cross-sector collaboration are the most influential drivers of digital-enabled decarbonization, while foundational elements such as smart energy systems and digital infrastructure act as key enablers. The DEMATEL-ISM approach facilitates a system-level understanding of the causal relationships and strategic priorities among the CSFs, offering actionable insights for urban planners, policymakers, and stakeholders committed to sustainable digital transformation and carbon neutrality. Full article

Amid intensifying global ESG regulations and the expanding influence of green finance, China’s digital economy policies have emerged as key institutional instruments for promoting corporate sustainability. Leveraging the implementation of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment, this study utilizes panel data of Chinese listed firms from 2009 to 2023 and applies multi-period Difference-in-Differences (DID) and Spatial DID models to rigorously identify the policy’s effects on corporate ESG performance. Empirical results indicate that the impact of digital economy policy is not exerted through a direct linear pathway but operates via three institutional mechanisms, enhanced information transparency, eased financing constraints, and expanded fiscal support, collectively constructing a logic of “institutional embedding–governance restructuring.” Moreover, disruptive technological innovation significantly amplifies the effects of the transparency and fiscal mechanisms, but exhibits no statistically significant moderating effect on the financing constraint pathway, suggesting a misalignment between innovation heterogeneity and financial responsiveness. Further heterogeneity analysis confirms that the policy effect is concentrated among firms characterized by robust governance structures, high levels of property rights marketization, and greater digital maturity. This study contributes to the literature by developing an integrated moderated mediation framework rooted in institutional theory, agency theory, and dynamic capabilities theory. The findings advance the theoretical understanding of ESG policy transmission by unpacking the micro-foundations of institutional response under digital policy regimes, while offering actionable insights into the strategic alignment of digital transformation and sustainability-oriented governance. Full article

Improving the green innovation quality (GIQ) of center cities is crucial to achieve sustainable urban agglomeration development. Utilizing data on green patent citations and economic indicators across cities in the Yangtze River Delta urban agglomeration (YRD) from 2003 to 2022, this research examines the influence of center cities’ GIQ on the economic performance of peripheral municipalities. The results show the following: (1) Center cities’ GIQ exerts a significant suppressive effect on peripheral cities’ economic growth overall. Heterogeneity analysis uncovers a distance-dependent duality. GIQ stimulates growth in proximate cities (within 300 km) but suppresses it beyond this threshold. This spatial siphoning effect is notably amplified in national-level center cities. (2) Mechanisms suggest that GIQ accelerates the outflow of skilled labor in peripheral cities through factor agglomeration and industry transfer mechanisms. Concurrently, it impedes the gradient diffusion of urban services, collectively hindering peripheral development. (3) Increased government green attention (GGA) and industry–university–research cooperation (IURC) in centers can mitigate these negative impacts. This paper contributes to the theoretical discourse on center cities’ spatial externalities within agglomerations and offers empirical support and policy insights for the exertion of spillover effects of high-quality green innovation from center cities and the sustainable development of urban agglomeration. Full article

In the digital era, understanding the intention to use generative AI is critical, as it enhances productivity, transforms workflows, and enables humans to focus on higher-value tasks. Drawing upon the unified theory of acceptance and use of technology (UTAUT) and the technology threat avoidance theory (TTAT), this research integrates perceived AI literacy into the AI acceptance–avoidance framework as a central variable. This study gathered 583 valid survey responses from China and validated its model using a dual-phase, combined method that integrates structural equation modeling and artificial neural networks. Research findings indicate that the model explains 51.6% of the variance in generative AI usage intention. Except for social influence, all variables within the extended framework significantly impact the usage intention, with perceived AI literacy being the strongest predictor (β = 0.33, p < 0.001). Additionally, perceived AI literacy mitigates the adverse effect of perceived threats on the intention to use AI. Practical implications suggest that enterprises adopt a tiered strategy, as follows: maximize perceived benefits by integrating AI skills into reward systems and providing task-automation training; minimize perceived costs through dedicated technical support and transparent risk mitigation plans; and cultivate AI literacy via progressive learning paths, advancing from data analysis to innovation. Full article

Recent advancements in deep learning have significantly enhanced brain tumor segmentation from MRI data, providing valuable support for clinical diagnosis and treatment planning. However, challenges persist in effectively integrating prior medical knowledge, capturing global multimodal features, and accurately delineating tumor boundaries. To address these challenges, the Hybrid Network for Multimodal Brain Tumor Segmentation (HN-MBTS) is proposed, which incorporates prior medical knowledge to refine feature extraction and boundary precision. Key innovations include the Two-Branch, Two-Model Attention (TB-TMA) module for efficient multimodal feature fusion, the Linear Attention Mamba (LAM) module for robust multi-scale feature modeling, and the Residual Attention (RA) module for enhanced boundary refinement. Experimental results demonstrate that this method significantly outperforms existing approaches. On the BraT2020 and BraT2023 datasets, the method achieved average Dice scores of 87.66% and 88.07%, respectively. These results confirm the superior segmentation accuracy and efficiency of the approach, highlighting its potential to provide valuable assistance in clinical settings. Full article

Background/Objectives: In many parts of the world, pharmacists hold the primary responsibility for providing safe and effective pharmacotherapy. A key aspect is the availability of appropriate medicines for each individual patient. When industrially manufactured medicines are unsuitable or unavailable, pharmacists can prepare tailor-made medicines. While this principle applies globally, practices vary between countries. In the Netherlands, the preparation of medicines in pharmacies is well-established and integrated into routine healthcare. This narrative review explores the role and significance of extemporaneous compounding, pharmacy preparations and related product care in the Netherlands. Methods: Pharmacists involved in pharmacy preparations across various professional sectors, including community and hospital pharmacies, central compounding facilities, academia, and the professional pharmacists’ organisation, provided detailed and expert insights based on the literature and policy documents while also sharing their critical perspectives. Results: We present arguments supporting the need for pharmacy preparations and examine their position and role in community and hospital pharmacies in the Netherlands. Additional topics are discussed, including the regulatory and legal framework, outsourcing, quality assurance, standardisation, education, and international context. Specific pharmacy preparation topics, often with a research component and a strong focus on product care, are highlighted, including paediatric dosage forms, swallowing difficulties and feeding tubes, hospital-at-home care, reconstitution of oncolytic drugs and biologicals, total parenteral nutrition (TPN), advanced therapy medicinal products (ATMPs), radiopharmaceuticals and optical tracers, clinical trial medication, robotisation in reconstitution, and patient-centric solid oral dosage forms. Conclusions: The widespread acceptance of pharmacy preparations in the Netherlands is the result of a unique combination of strict adherence to tailored regulations that ensure quality and safety, and patient-oriented flexibility in design, formulation, and production. This approach is further reinforced by the standardisation of a broad range of formulations and procedures across primary, secondary and tertiary care, as well as by continuous research-driven innovation to develop new medicines, formulations, and production methods. Full article

Based on our question, “Are reels/short-videos the real deal in video-based learning?” this study explores the effectiveness of short (around 2 min) video-based learning in engaging medical students from the second large medical Portuguese school. With the increasing integration of digital tools in education, video content has emerged as a dynamic method to enhance learning experiences. This cross-sectional survey was conducted by using anonymous self-administered questionnaires, prepared with reference to previous studies, and distributed to 264 informed students who voluntarily agreed to participate. This sample represented 75.5% of the students attending the classes. The questionnaires included topics related to the 65 short videos about practical classes, as well as the students’ learning preferences. The collected data were analyzed using descriptive and comparative statistics. The students considered that the content and format of the videos were adequate (99.6% and 100%, respectively). Specifically, the videos helped the students to better understand the practical classes, consolidate and retain the practical content, and simplify the study for the exams. Additionally, the videos were praised for their high-quality audiovisual content, being innovative, complete, concise, short and/or adequate, or better than other formats such as printed information. The combination of written and audiovisual support materials for teaching and studying is important and has been shown to improve students’ performance. This pedagogical methodology is well-suited for the current generation of students, aiding not only in study and exam preparation but also in remote learning. Full article

In this study, we focus on solving the nonlinear time-fractional Hirota–Satsuma coupled Korteweg–de Vries (KdV) and modified Korteweg–de Vries (MKdV) equations, using the Yang transform iterative method (YTIM). This method combines the Yang transform with a new iterative scheme to construct reliable and efficient solutions. Readers can understand the procedures clearly, since the implementation of Yang transform directly transforms fractional derivative sections into algebraic terms in the given problems. The new iterative scheme is applied to generate series solutions for the provided problems. The fractional derivatives are considered in the Caputo sense. To validate the proposed approach, two numerical examples are analysed and compared with exact solutions, as well as with the results obtained from the fractional reduced differential transform method (FRDTM) and the q-homotopy analysis transform method (q-HATM). The comparisons, presented through both tables and graphical illustrations, confirm the enhanced accuracy and reliability of the proposed method. Moreover, the effect of varying the fractional order is explored, demonstrating convergence of the solution as the order approaches an integer value. Importantly, the time-fractional Hirota–Satsuma coupled KdV and modified Korteweg–de Vries (MKdV) equations investigated in this work are not only of theoretical and computational interest but also possess significant implications for achieving global sustainability goals. Specifically, these equations contribute to the Sustainable Development Goal (SDG) “Life Below Water” by offering advanced modelling capabilities for understanding wave propagation and ocean dynamics, thus supporting marine ecosystem research and management. It is also relevant to SDG “Climate Action” as it aids in the simulation of environmental phenomena crucial to climate change analysis and mitigation. Additionally, the development and application of innovative mathematical modelling techniques align with “Industry, Innovation, and Infrastructure” promoting advanced computational tools for use in ocean engineering, environmental monitoring, and other infrastructure-related domains. Therefore, the proposed method not only advances mathematical and numerical analysis but also fosters interdisciplinary contributions toward sustainable development. Full article

In light of pressing global nutritional needs, the valorization of agri-food waste constitutes a vital strategy for enhancing human health and nutrition, while simultaneously supporting planetary health. This integrated approach is increasingly indispensable within sustainable and equitable food systems. Recently, a sustainability-driven focus has shifted attention toward the valorization of the agri-food by-products as rich sources of bioactive compounds useful in preventing or treating chronic diseases. Agri-food by-products, often regarded as waste, actually hold great potential as they are rich in bioactive components, dietary fiber, and other beneficial nutrients from which innovative food ingredients, functional foods, and even therapeutic products are developed. This review aims to provide a comprehensive analysis of the current advances in recovering and applying such compounds from agri-food waste, with a particular focus on their roles in human health, sustainable packaging, and circular economy strategies. Methods: This review critically synthesizes recent scientific literature on the extraction, characterization, and utilization of bioactive molecules from agri-food by-products. After careful analysis of the PubMed and Scopus databases, only English-language articles from the last 10 years were included in the final narrative review. The analysis also encompasses applications in the nutraceutical, pharmaceutical, and food packaging sectors. Results: Emerging technologies have enabled the efficient and eco-friendly recovery of compounds such as polyphenols, carotenoids, and dietary fibers that demonstrate antioxidant, antimicrobial, and anti-inflammatory properties. These bioactive compounds support the development of functional foods and biodegradable packaging materials. Furthermore, these valorization strategies align with global health trends by promoting dietary supplements that counteract the effects of the Western diet and chronic diseases. Conclusions: Valorization of agri-food by-products offers a promising path toward sustainable development by reducing waste, enhancing public health, and driving innovation. This strategy not only minimizes waste and supports sustainability, but also promotes a more nutritious and resilient food system. Full article

As a key engine driving China’s green financial transformation, the Green Financial Reform and Innovation Pilot Zones have demonstrated significant achievements in enhancing the capacity of financial services to support green real economies, preventing and mitigating green financial risks, and bolstering national and urban economic resilience. On this basis, a spatial Markov chain model is applied to further analyze the economic toughness of prefecture-level cities. This study treats the establishment of these pilot zones as a quasi-natural experiment, using panel data from 269 prefecture-level cities in China from 2013 to 2023 and employing a multi-period difference-in-differences (DID) model to empirically examine the impact of green financial reform on urban economic resilience and its underlying mechanisms. The results reveal that the establishment of these pilot zones significantly enhances urban economic resilience. Specifically, green financial reforms primarily improve urban economic resilience by increasing credit accessibility and capital allocation efficiency in the pilot cities. Furthermore, the policy effects are more pronounced in large cities and resource-dependent cities compared to small and medium-sized cities and non-resource-dependent cities, with stronger impacts observed in southern and coastal regions than in northern inland areas. Additionally, the policy effects are significantly greater in environmentally prioritized cities than in non-prioritized cities. By integrating green financial reforms and urban economic resilience into a unified analytical framework, this study provides valuable insights for policymakers to refine green financial strategies and design resilience-enhancing policies. Full article

This paper presents a case study of the Local Care Ecosystems developed by the provincial government of Gipuzkoa (Basque Country, Spain) to strengthen coordination between social services, health services, and community-based initiatives at the municipal level. The initiative seeks to personalize care, enhance service integration, and support community-based care with the overarching goal of improving the quality of life for older adults living at home. These ecosystems incorporate social, institutional, and technological innovations aimed at supporting individuals who are frail or vulnerable throughout the care cycle. At present, 18 Local Care Ecosystems are active, providing services to 1202 people over the age of 65 and 167 families. The model addresses a growing global challenge linked to population aging, which has led to increasing demand for care and support services that are often fragmented, under-resourced, and constrained by outdated regulatory frameworks. These structural issues can compromise both the quality and efficiency of care for dependent individuals. Based on the findings, the paper offers policy recommendations to support the transfer and adaptation of this model, with the aim of improving the well-being of older adults who wish to remain in their own homes. Full article